To improve image quality under low illumination conditions, a novel low-light image enhancement method is proposed in this paper based on multi-illumination estimation and multi-scale fusion (MIMS). Firstly, the illumination is processed by contrast-limited adaptive histogram equalization (CLAHE), adaptive complementary gamma function (ACG), and adaptive detail preserving S-curve (ADPS), respectively, to obtain three components. Then, the fusion-relevant features, exposure, and color contrast are selected as the weight maps. Subsequently, these components and weight maps are fused through multi-scale to generate enhanced illumination. Finally, the enhanced images are obtained by multiplying the enhanced illumination and reflectance. Compared with existing approaches, this proposed method achieves an average increase of 0.81% and 2.89% in the ​structural similarity index measurement (SSIM) and peak signal-to-noise ratio (PSNR), and a decrease of 6.17% and 32.61% in the natural image quality evaluator (NIQE) and gradient magnitude similarity deviation (GMSD), respectively.